Direct Orthogonal Neighborhood Preserving Discriminant Analysis

2011 ◽  
Vol 179-180 ◽  
pp. 1254-1259
Author(s):  
Yu Rong Lin ◽  
Qiang Wang
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Recognition Rate ◽  
Feature Space ◽  
Experimental Results ◽  
New Method ◽  
High Dimensional ◽  
Singularity Problem

Several orthogonal feature extraction algorithms based on local preserving projection have recently been proposed. However, these methods don’t address the singularity problem in the high dimensional feature space,which means that the eigen-equation of orthogonal feature extraction algorithms cannot be solved directly. In this paper, we present a new method called Direct Orthogonal Neighborhood Preserving Discriminant Analysis (DONPDA), which is able to extract all the orthogonal discriminant vectors simultaneously in the high-dimensional feature space and does not suffer the singularity problem. Experimental results on ORL database indicate that the proposed DONPDA method achieves higher recognition rate than the ONPDA method and other some existing orthogonal feature extraction algorithms.

Tensor Linear Discriminant Analysis

2011 ◽  
pp. 172-201
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Computational Efficiency ◽  
Curse Of Dimensionality ◽  
Experimental Results ◽  
Singularity Problem ◽  
Biometric Data ◽  
Linear Discriminant ◽  
Important Method

Linear discriminant analysis is a very effective and important method for feature extraction. In general, image matrices are often transformed into vectors prior to feature extraction, which results in the curse of dimensionality when the dimensions of matrices are huge. In this chapter, classical LDA and its several variants are introduced. In some sense, the variants of LDA can avoid the singularity problem and achieve computational efficiency. Experimental results on biometric data show the usefulness of LDA and its variants in some cases.

Optimal multi-kernel local fisher discriminant analysis for feature dimensionality reduction and fault diagnosis

2021 ◽  
pp. 1748006X2110093
Author(s):  
Qing Zhang ◽  
Heng Li ◽  
Xiaolong Zhang ◽  
Haifeng Wang
Keyword(s):  
Fault Diagnosis ◽  
Discriminant Analysis ◽  
Feature Space ◽  
High Dimensional ◽  
Fisher Discriminant Analysis ◽  
Vibration Signals ◽  
Fisher Discriminant ◽  
Local Fisher Discriminant Analysis ◽  
Diagnosis Accuracy ◽  
Diagnosis Model

To achieve a more desirable fault diagnosis accuracy by applying multi-domain features of vibration signals, it is significative and challenging to refine the most representative and intrinsic feature components from the original high dimensional feature space. A novel dimensionality reduction method for fault diagnosis is proposed based on local Fisher discriminant analysis (LFDA) which takes both label information and local geometric structure of the high dimensional features into consideration. Multi-kernel trick is introduced into the LFDA to improve its performance in dealing with the nonlinearity of mapping high dimensional feature space into a lower one. To obtain an optimal diagnosis accuracy by the reduced features of low dimensionality, binary particle swarm optimization (BPSO) algorithm is utilized to search for the most appropriate parameters of kernels and K-nearest neighbor (kNN) recognition model. Samples with labels are used to train the optimal multi-kernel LFDA and kNN (OMKLFDA-kNN) fault diagnosis model to obtain the optimal transformation matrix. Consequently, the trained fault diagnosis model implements the recognition of machinery health condition with the most representative feature space of vibration signals. A bearing fault diagnosis experiment is conducted to verify the effectiveness of proposed diagnostic approach. Performance comparison with some other methods are investigated, and the improvement for fault diagnosis of the proposed method are confirmed in different aspects.

scholarly journals A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Chenchen Huang ◽  
Wei Gong ◽  
Wenlong Fu ◽  
Dongyu Feng
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Recognition Rate ◽  
Original Method ◽  
Speech Emotion Recognition ◽  
High Dimensional ◽  
Svm Classifier ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Multiple Classifier

Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method.

scholarly journals Instruction2vec: Efficient Preprocessor of Assembly Code to Detect Software Weakness with CNN

2019 ◽  
Vol 9 (19) ◽  
pp. 4086 ◽  
Author(s):  
Yongjun Lee ◽  
Hyun Kwon ◽  
Sang-Hoon Choi ◽  
Seung-Ho Lim ◽  
Sung Hoon Baek ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Experimental Results ◽  
New Method ◽  
Binary Analysis ◽  
Vulnerability Detection ◽  
Security Vulnerabilities ◽  
Assembly Code ◽  
Detection Techniques ◽  
Software Vulnerabilities ◽  
Analysis Technique

Potential software weakness, which can lead to exploitable security vulnerabilities, continues to pose a risk to computer systems. According to Common Vulnerability and Exposures, 14,714 vulnerabilities were reported in 2017, more than twice the number reported in 2016. Automated vulnerability detection was recommended to efficiently detect vulnerabilities. Among detection techniques, static binary analysis detects software weakness based on existing patterns. In addition, it is based on existing patterns or rules, making it difficult to add and patch new rules whenever an unknown vulnerability is encountered. To overcome this limitation, we propose a new method—Instruction2vec—an improved static binary analysis technique using machine. Our framework consists of two steps: (1) it models assembly code efficiently using Instruction2vec, based on Word2vec; and (2) it learns the features of software weakness code using the feature extraction of Text-CNN without creating patterns or rules and detects new software weakness. We compared the preprocessing performance of three frameworks—Instruction2vec, Word2vec, and Binary2img—to assess the efficiency of Instruction2vec. We used the Juliet Test Suite, particularly the part related to Common Weakness Enumeration(CWE)-121, for training and Securely Taking On New Executable Software of Uncertain Provenance (STONESOUP) for testing. Experimental results show that the proposed scheme can detect software vulnerabilities with an accuracy of 91% of the assembly code.

UNCORRELATED PROJECTION DISCRIMINANT ANALYSIS AND ITS APPLICATION TO FACE IMAGE FEATURE EXTRACTION

2003 ◽  
Vol 17 (08) ◽  
pp. 1325-1347 ◽  
Author(s):  
JIAN YANG ◽  
JING-YU YANG ◽  
ALEJANDRO F. FRANGI ◽  
DAVID ZHANG
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Face Image ◽  
Desirable Property ◽  
Experimental Results ◽  
Image Feature ◽  
Analysis Method ◽  
Two Stage ◽  
Image Feature Extraction ◽  
Projection Analysis

In this paper, a novel image projection analysis method (UIPDA) is first developed for image feature extraction. In contrast to Liu's projection discriminant method, UIPDA has the desirable property that the projected feature vectors are mutually uncorrelated. Also, a new LDA technique called EULDA is presented for further feature extraction. The proposed methods are tested on the ORL and the NUST603 face databases. The experimental results demonstrate that: (i) UIPDA is superior to Liu's projection discriminant method and more efficient than Eigenfaces and Fisherfaces; (ii) EULDA outperforms the existing PCA plus LDA strategy; (iii) UIPDA plus EULDA is a very effective two-stage strategy for image feature extraction.

scholarly journals Membership-Degree Preserving Discriminant Analysis with Applications to Face Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Zhangjing Yang ◽  
Chuancai Liu ◽  
Pu Huang ◽  
Jianjun Qian
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
High Dimensional ◽  
Membership Degree ◽  
Extraction Techniques ◽  
Fisher Criterion ◽  
Extraction Algorithm ◽  
Definition Of

In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data. In this paper, we propose a novel feature extraction algorithm called membership-degree preserving discriminant analysis (MPDA) based on the fisher criterion and fuzzy set theory for face recognition. In the proposed algorithm, the membership degree of each sample to particular classes is firstly calculated by the fuzzyk-nearest neighbor (FKNN) algorithm to characterize the similarity between each sample and class centers, and then the membership degree is incorporated into the definition of the between-class scatter and the within-class scatter. The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied. Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm.

An E-nose and Convolution Neural Network based Recognition Method for Processed Products of Crataegi Fructus

2020 ◽  
Vol 23 ◽  
Author(s):  
Tianshu Wang ◽  
Yanpin Chao ◽  
Fangzhou Yin ◽  
Xichen Yang ◽  
Chenjun Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Electronic Nose ◽  
Experimental Results ◽  
Convolution Neural Network ◽  
New Method ◽  
Recognition Method ◽  
Processed Products

Background: The identification of Fructus Crataegi processed products manually is inefficient and unreliable. Therefore, how to identify the Fructus Crataegis processed products efficiently is important. Objective: In order to efficiently identify Fructus Grataegis processed products with different odor characteristics, a new method based on an electronic nose and convolutional neural network is proposed. Methods: First, the original smell of Fructus Grataegis processed products is obtained by using the electronic nose and then preprocessed. Next, feature extraction is carried out on the preprocessed data through convolution pooling layer Results: The experimental results show that the proposed method has higher accuracy for the identification of Fructus Grataegis processed products, and is competitive with other machine learning based methods. Conclusion: The method proposed in this paper is effective for the identification of Fructus Grataegi processed products.

SAR Image Recognition Combined Bidirectional 2DPCA with PCA

2013 ◽  
Vol 756-759 ◽  
pp. 4041-4044 ◽  
Author(s):  
De Gong Wang ◽  
Zuo Long Yang ◽  
Shuo Chang
Keyword(s):  
Experimental Data ◽  
Principal Component Analysis ◽  
Feature Extraction ◽  
Image Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Column Vector ◽  
Experimental Result ◽  
High Dimensional ◽  
Sar Image

The method of Principal Component Analysis (PCA) needs to convert image matrix to high-dimensional column vector used in feature extraction. The 2-dimensional PCA (2DPCA) offsets disadvantages of PCA. However, 2DPCA compresses image along the rows or columns only, the number of features is still large. In order to solve the above problems, bidirectional 2DPCA was used to compress image matrix along row and column meanwhile, then use PCA reduce the number of computations and feature dimensions. Three kinds of ground static military targets images acquired by SAR were used as the experimental data. The experimental result shows that, the method of SAR image recognition presented by this paper reduced the dimensions of feature matrix and raised the recognition rate.

scholarly journals DWT-based Illumination Normalization and Feature Extraction for Enhanced Face Recognition

2012 ◽  
Vol 1 (4) ◽  
pp. 483 ◽  
Author(s):  
Manikantan K ◽  
Milan S Shet ◽  
Minal Patel ◽  
Ramachandran S
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Rate ◽  
Substantial Reduction ◽  
Feature Space ◽  
Binary Particle Swarm Optimization ◽  
Discrete Wavelet ◽  
Feature Subset ◽  
Lighting Conditions ◽  
Optimal Feature Subset

Face recognition (FR) under varying lighting conditions is challenging, and exacting illumination invariant features is an effective approach to solve this problem. In this paper, we propose to utilize Discrete Wavelet Transform (DWT) for normalizing the illumination variance in images as well as for feature extraction. Individual stages of the FR system are examined and an attempt is made to improve each stage. A Binary Particle Swarm Optimization (BPSO) based feature selection algorithm is used to search the feature space for the optimal feature subset. Experimental results, obtained by applying the proposed algorithm on YaleB and Color FERET face databases, show that the proposed system outperforms other FR systems. A significant increase in the recognition rate and a substantial reduction in the number of features is observed. Dimensionality reduction obtained is more than 99% for both YaleB and Color FERET databases.

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